Day: January 14, 2020

AI Natural Language Processing

Pretrained Deep Bidirectional Transformers (BERT) for Language Understanding

Here’s a talk by Danny Luo Pre-training of Deep Bidirectional Transformers for Language Understanding We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all […]

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AI Natural Language Processing Python

Transfer Learning – Entering a New Era in NLP

Malte Pietsch delivers this keynote on “Transfer Learning – Entering a new era in NLP” at PyData Warsaw 2019 Transfer learning has been changing the NLP landscape tremendously since the release of BERT one year ago. Transformers of all kinds have emerged, dominate most research leaderboards and have made their way into industrial applications. In […]

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Why TinyML is a giant opportunity
AI IoT TensorFlow

Why TinyML is a giant opportunity

There are 250 billion micro-controllers in the world today. 28.1 billion units were sold in 2018 alone, and IC Insights forecasts annual shipment volume to grow to 38.2 billion by 2023. What if they all became smart? How would that change our world? From venturebeat.com: TinyML broadly encapsulates the field of machine learning technologies capable of […]

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Where will data science and audience insights take us in 2020?
AI

Where will data science and audience insights take us in 2020?

Since it’s still January, we can still make predictions for the year. 2020 will see further democratization of machine learning tools and a lower point of entry for their usage. This will make data science/AI even more commonplace not only among top tech companies, but also small and medium-sized businesses across various verticals. However, one […]

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AI’s Invading Hollywood, Bringing Us Closer to ‘Execubots’ - Nerdist
AI

AI is Invading Hollywood

Will “Network Execubots” decide what films and TV shows get made? The Hollywood Reporter has just reported that Warner Bros. has signed a deal with a tech company to implement an “AI-driven film management” system. The system, which may sound more like an administrative tool than an industry game changer, will help the major studio decide […]

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AI

Stanford AI Course Lecture 1

Associate Professor Percy Liang Associate Professor of Computer Science and Statistics and delivers this introductory lecture on AI.

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Data Science Mathematics

Graph Theory Tutorial

This full 6 hour+ course provides a complete introduction to Graph Theory algorithms in computer science. Code: https://github.com/williamfiset/algorithms Slides: https://github.com/williamfiset/Algorithms/tree/master/slides/graphtheory Course created by William Fiset. Check out his YouTube channel: https://www.youtube.com/channel/UCD8yeTczadqdARzQUp29PJw ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Graph Theory Introduction ⌨️ (0:13:53) Problems in Graph Theory ⌨️ (0:23:15) Depth First Search Algorithm ⌨️ (0:33:18) Breadth […]

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Data Developer

Entity Framework Best Practices

Tim Corey explores Entity Framework, an amazing set of tooling around data access. With EFCore, that tooling becomes even more powerful. So why is it that I still don’t recommend that people use EFCore? In this video, he walks you through the best practices of Entity Framework and EFCore and point out the pitfalls to […]

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Azure Developer

Messaging with Azure Event Hubs

In this episode, Serkant Karaca and Shubha Vijayasarathy from the Azure Event Hubs team talk about how and when to use Azure Event Hubs as the messaging component in our .NET applications. They’ll discuss use cases, cover topics like partitioning  and also show how to use the .NET SDK for Event Hubs. Useful Links Azure […]

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Machine Learning

Reliable Machine Learning Systems

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems. Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. […]

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